![]() In the case that the file wouldn't be accessible for any reason, the Failure would contain the message of some I/O exception. Printing the result of the above example will yield the following notice how we receive the first error that occurred, which happens when we try to extract Rambo's age. ![]() Later, when we want to know if our operation succeeded, we perform the type-check to either extract the result value from a Success or an error from the Failure. Since the map operation on a Try also catches any errors that might occur, there is nothing that can really go wrong. Spot,7,Dog Alice,14,Cat Rambo,Dog Mike,3,Raccoon The Try encapsulates the operation we want to execute and holds on to the result, the List containing the lines from the file.Ĭommonly, you would want to perform an operation on the data, in this example, we want to convert each line from the file into a Pet object, and we can see that the toPet function involves a few operations that can fail.įails include converting a string to an integer to resolve the age and looking up an enum by a string to get the PetType.įor reference, the content of the file I’m using for this exercise looks like this, we can see that with the above code the last two lines will cause issues, Rambo has no age and Mike is not a valid PetType. Let’s look at a slightly more involved example of using a Try in Kotlin, before looking at the implementation.Ī typical exercise, we want to read the lines from a CSV file which could throw all kinds of I/O exceptions and, usually, we’d surround this either with a try-catch or just let any potential exception fly and terminate the program or jump into the next catch-block further up the stack if there is any. This has the effect that you can keep working with the Try, passing it around and manipulating the data inside without having to determine whether you are dealing with a Success or a Failure until the very last moment when the final result is ultimately required, kind of like Schrödinger’s value. That is not all though, the Try also provides a set of higher-order functions that allow transforming the value it contains. When using the Try as the return type of a function, it indicates to the caller that it might fail and the result could be an error, since, for example, a return type that would usually be a String now becomes a Try. This provides you with the benefit of encapsulating any error that might occur and having an abstraction over potential failure. Success("Hello, World") Failure("Not Today!") To know which one you’re dealing with, you can perform a type check. As expected, the first is a Success holding a string value and the second is a Failure containing the exception. The result you would see in the console is the following. Luckily, it is quite simple to implement, and Kotlin, with its language features, lends itself very well to that concept. I liked Kotlins built-in null-type with a special syntax, and Java now has its Optional type in the standard library, but both lack an equivalent for error handling. Since Kotlin has decent support for the functional programming paradigm, the first thing I did when learning the language was trying to imitate what I learned from Scala. They respectively either hold the resulting value of the function or the error that is potentially created. The Try executes the function, catching any errors that occur and creates one of its two subtypes, a Success or a Failure. ![]() The basic idea is that you wrap a function that you want to execute but which might throw an error in a Try. The language features a few algebraic data types in its standard library, one of which provides an alternative for the imperative try-catch block, the aptly named Try. When I made my initial attempts at functional programming with Scala, I remember that what I liked the most was how the language enabled you to handle null-values and errors.Įven though you can write a regular try-catch block in Scala, you can also choose not to, and I never once did. Handling errors is often a bothersome task. To handle exceptions, we have to surround function calls with try-catch blocks that make the code messy, often just to perform logging or to free resources and to then re-throw the exception, or even worse, return a null value. Oftentimes, the caller of a function doesn’t even know that it might throw an exception and this lack of referential transparency makes code more difficult to understand and can lead to unforeseen bugs and issues. I really don't like try-catch blocks and how exceptions are dealt with in many object-oriented programming languages.
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